Fall 2016

SEM217: Ryan Copus and Hannah Laqueur, UC Berkeley: Machines Learning Justice: A New Approach to the Problems of Inconsistency and Bias in Adjudication

Tuesday, October 25th @ 11:00-12:30 PM (639 Evans Hall)

Machines Learning Justice: A New Approach to the Problems of Inconsistency and Bias in Adjudication

Ryan Copus and Hannah Laqueur, UC Berkeley

Abstract: We offer a two-step algorithmic approach to the problems of inconsistency and bias in legal decision making. First, we propose a new tool for reducing inconsistency: Judgmental Bootstrapping Models (“JBMs”) built with machine learning methods. JBMs, by providing judges with...

SEM217: Thomas Idzorek, CFA, Head of Investment Methodology and Economic Research at Morningstar: Popularity: A Unifying Asset Pricing Framework?

Tuesday, October 18th @ 11:00-12:30 PM (639 Evans Hall)

Popularity: A Unifying Asset Pricing Framework?

Thomas Idzorek, CFA, Head of Investment Methodology and Economic Research at Morningstar

In a 2014 article, Thomas Idzorek and Roger Ibbotson introduced popularity as an asset pricing framework....

SEM217: Robert M. Anderson, CDAR Co-Director: PCA with Model Misspecification

Tuesday, November 29th @ 11:00-12:30 PM (639 Evans Hall)

PCA with Model Misspecification

Robert M. Anderson, CDAR Co-Director

In this project with UC Berkeley Ph.D. Candidate Farzad Pourbabaee, Principal Component Analysis (PCA) relies on the assumption that the data being analyzed is IID over the estimation window. PCA is frequently applied to financial data, such as stock returns, despite the fact that these data exhibit obvious and substantial changes in volatility. We show that...

Baeho Kim, Korea University Business School: Stochastic Intensity Margin Modeling of Credit Default Swap Portfolios

Abstract: We consider the problem of initial margin (IM) modeling for portfolios of credit default swaps (CDS) from the perspective of a derivatives Central Counterparty (CCP). The CCPs' IM models in practice are based on theoretically-unfounded direct statistical modeling of CDS spreads. Using a reduced-form approach, our IM model based on stochastic default intensity prices the portfolio constituents in a theoretically meaningful way and shows that statistical IM models can underestimate CCPs' collateral requirements. In addition, our proposed Affine jump-diffusion intensity modeling...

Alex Papanicolaou: Testing Local Volatility in Short Rate Models

The first CRMR Risk Seminar of Fall 2016 features work by CDAR Postdoc Alex Papanicolaou. Abstract: We provide a simple and easy to use goodness-of-fit test for the misspecification of the volatility function in diffusion models. The test uses power variations constructed as functionals of discretely observed diffusion processes. We introduce an orthogonality condition which stabilizes the limit law in the presence of parameter estimation and avoids the necessity for a bootstrap procedure that reduces performance and leads to complications associated with the structure of the diffusion...

Bjorn Flesaker, Adjunct Professor at Courant Institute of Mathematical Sciences, NYU: Some Empirical Properties of a Bounded Interest Rate Model

We consider the two-factor version of a family of time-homogeneous interest rate models introduced by Cairns (Math Finance, 2004) in the Flesaker-Hughston positive interest framework. Specifically, we calibrate the model to cross-sectional USD swap and swaption market data, and we compare the corresponding model implied dynamics to that of the swap market rates via PCA. We investigate whether allowing a non-zero lower bound improves the model fit. The model dynamics are reformulated as a two-dimensional Ito process for the short rate and the consol rate (the par yield on a bond with no...

Ram Akella, University of California, Berkeley School of Information and TIM/CITRIS/UCSC: Dynamic Multi-modal and Real-Time Causal Predictions and Risks

There are three major trends in prediction and risk analytics. We describe our research on two fronts and speculate on the third. We do this in the context of healthcare analytics and computational advertising at Silicon Valley firms. We first describe prediction and risk analytics using combined multi-modal numerical data (from vitals and labs) and text data (from notations by doctors and nurses). We describe and analyze dynamic models of patient mortality probabilities, and integrate novel topic modeling to account for topic constraints, and demonstrate superior performance on Intensive...

Mark Flood, Office of Financial Research: Measures of Financial Network Complexity: A Topological Approach

We present a general definition of complexity appropriate for financial counterparty networks and derive several topologically based implementations. These range from simple and obvious metrics to others that are more mathematically subtle. It is important to tailor a complexity measure to the specific context in which it is used. This paper introduces measures of the complexity of search and netting in dealer markets. We define measures of line graph homology and collateral line graph homology that are sensitive to network interactions, such as collateral commingling and interdependent...

Samim Ghamami, Office of Financial Research: Does OTC Derivatives Reform Incentivize Central Clearing?

Joint Work with Paul Glasserman Abstract: The reform program for the over-the-counter (OTC) derivatives market launched by the G-20 nations in 2009 seeks to reduce systemic risk from OTC derivatives. The reforms require that standardized OTC derivatives be cleared through central counterparties (CCPs), and they set higher capital and margin requirements for non-centrally cleared derivatives. Our objective is to gauge whether the higher capital and margin requirements adopted for bilateral contracts create a cost incentive in favor of central clearing, as intended. We introduce a model of...

Daniel Mantilla-Garcia, Optimal Asset Management: Disentangling the Volatility Return: A Predictable Return Driver of Any Diversified Portfolio

Abstract: The long-term performance of any portfolio can be decomposed as the sum of the weighted average long-term return of its assets plus the volatility return of the portfolio. The volatility return represents a larger proportion of the total return of portfolios with more homogeneous assets, such as stock factor portfolios. We unveil a direct relationship between the volatility return, and the cross-sectional variance of stock returns, as well as with the average idiosyncratic variance of the stocks in the portfolio. Furthermore, we introduce a strategy that maximizes the volatility...